For the institutions included in this engagement?s sample, we compared their final composite score with the score they received using Dun & Bradstreet?s (D&B) Financial Stress Class and Credit Score Class.

D&B?s Financial Stress Class are statistically derived values that are comprised to reflect the likelihood of a business ceasing operations without paying creditors in full, or seeking bankruptcy protection within the next eighteen months. The Financial Stress Class relies on both financial and non-financial information including age of business, payment trends, financial ratios, and public records. Their Credit Score Class is designed to assess the probability of a firm paying its bills in a severely delinquent manner (90+ days past terms) over the next twelve months. Elements of information used for the Credit Score Class include demographic information, number of employees, payment experience, and age of payments in relation to terms.

In comparing the final composite scores calculated using KPMG's recommended methodology to scores derived from D&B's Financial Stress Class and Credit Score Class, certain arithmetic or statistical challenges arose. Namely, how does this methodology's scale equate to D&B's scales? How does one quantify the correlation between the three scales?

It appears, using non-statistical methods, that the correlation between this methodology's composite scores and D&B's indexes is higher (more closely correlated) than the correlation between the NPRM scores and D&B's indexes. Further, there appears to be a reasonable degree of correlation between this methodology's scores and D&B's indexes. It is important to note however, that the fundamental objective or purpose of D&B's indexes is different from this methodology's objective and the scores are compiled using mutually exclusive information. That is, non-financial information like payment history or age of business, used in the D&B indexes is not readily obtainable from general purpose financial statements upon which this methodology is based.

In developing the recommended methodology, KPMG considered a number of other methodologies, ratios and factors. A complete description of all methodologies, or components thereof, that were considered would be overly voluminous for this report and would provide information with limited usefulness. However, this section gives a brief overview of some of the items that KPMG considered and rejected in developing the recommended methodology.

There are a number of other organizations that employ various solvency predictor models. One popular model, the Edward I. Altman Model, uses financial ratios to generate a "z score" much like the way the recommended methodology generates a final composite score. The Edward I. Altman Model was rejected because of its complexity and the fact that it required market value information. To compute the z score, total market capitalization is needed, and that information is not available for many schools that participate in student financial assistance programs. That information is not readily obtainable from general purpose financial statements.

KPMG also considered various multiple tiered methodologies. With the multiple tier scenarios, schools would calculate their composite score using the three ratios from the NPRM methodology or the new recommended methodology then, if their composite score warranted, compute other select ratios. If the other ratio scores were strong enough, a school might pass the ratio test. This idea was rejected because, during the original project to develop the NPRM methodology, KPMG and ED determined that too many ratios made the methodology overly cumbersome and complex. The three ratios selected for this methodology provide a measure of institutions' total financial condition and the other ratios considered would not significantly improve on them.

Acid Test Ratio and other Working Capital Ratios - The acid test is a quick measure of highly liquid assets available to meet current obligations. A measure of liquidity is important in the analysis of financial condition. This ratio was eliminated for three reasons: 1) Expendable capital is a more important element than strictly liquid capital in assessing financial condition. 2) There is some dispute concerning the appropriate way to account for deferred revenue. For example, proprietary institutions can change the way deferred tuition revenue is reported (current vs. long-term) in order to meet the test. 3) Information to calculate the ratio for colleges and universities is difficult to extract from the GAAP financial statements because it is not a required disclosure. Working capital is defined as the difference between current assets and current liabilities. An excess would represent positive working capital available to satisfy obligations. The Primary Reserve ratio measures expendable net assets or owner's equity. In calculating the Primary Reserve ratio, non-expendable / non-liquid items are eliminated from owners? equity. The Primary Reserve Ratio was chosen because it is a more disciplined calculation and can be obtained in all cases.

Operating Income Ratios - An operating income ratio would measure income from operations, for example, as a percentage of net revenue. The results would only help answer part of the question: Did the institution live within its means for the fiscal year? We rejected this measure in favor of the Net Income ratio. The Net Income ratio measures the percentage of income compared to net revenues after operations and other non-operating items. This ratio represents a more complete picture of whether the institution spent more than it brought in during the fiscal year.

Debt to Equity (debt levels) - Like the viability ratio, this ratio requires debt to be calculated. More than 35% of proprietary institutions have no debt. Therefore, an equity to total assets ratio was utilized which provided a relative measure of leverage and could be calculated for all institutions regardless of whether the have long-term debt.

Cash Flow Ratios - Cash flow ratios were considered in developing the methodology. Several measures of cash provided from operations to cover debt payments and a net income ratio adjusted for non-cash expenses were considered. We found cash flow measures can easily be manipulated. For example, simply extending creditors from normal payment terms to 120 days will look like cash has been provided by operations, when in fact the trend of delayed vendor payment is not a positive indicator. KPMG, therefore, opted for an accrual based measure. In addition, we considered adjusting the net income ratio for non-cash items and setting the measure of strength based upon an adjusted net income ratio. This was rejected because of the objective to keep the final methodology as simple and user friendly as possible. However, the concept of considering non-cash expenses was used in setting the strength factors for the Net Income Ratio. Based upon the analysis of over 900 financial statements, we determined that depreciation expense was the largest non-cash item represented in the income statement (proprietary sector) and in the statement of activities (private non-profit sector). Based upon analysis, depreciation expense on average represented 3 - 4% of total revenues. In order to reflect this, the final strength factors for the Net Income ratio were adjusted (see Strength Factors chapter). The concept is that no credit should be given to an institution in any methodology if it is not producing cash income for a fiscal year. Therefore, the bottom of the range where no credit is given is a Net Income result of a 3 - 4% deficit depending on business sector.

Debt to Revenue and Debt Service Coverage - These ratios were considered secondary to the Equity ratio. They provide additional insight as to how the institution is managing its debt. The primary measure was determined to be how leveraged an institution is in the first place as measured by the Equity ratio.

Loan Default Rates - Information relating to default rates may be indirectly useful in assessing financial health but that information is generally not obtainable from general purpose financial statements. In addition, default rates are monitored under a different section of the Title IV regulations.

Institutional Longevity - Even if an institution has been in existence for 35 years, large financial deficits could still impact the financial condition. KPMG analyzed closed school information and found that in a sample of twenty-five closed institutions 12% were in existence for more than 25 years upon closure for financial reasons including failure of the loan default regulations.

Multiple Years' Average for Net Income - In measuring profitability, KPMG considered using a three year running average for the Net Income Ratio. That idea was rejected for a number three basic reasons. First, it would necessitate using financial statements from multiple years and make the methodology more cumbersome to administer. Second, the residual effect of all previous years' profitability is already reflected in institutions' balance sheets. In the proprietary sector, retained earnings, a component of total equity, identifies the amount of residual earnings that have been left in the institution. Third, and finally, the general unavailability of prior years? financial statements precluded KPMG from compiling comprehensive empirical data.

Comprehensive information about schools that had precipitously closed for financial reasons in prior years would have been very useful to KPMG in developing its recommendations. However, much of that information was not available because of the lack of availability of audited financial statements. Although ED had over one hundred statements from multiple years of closed school data, since audited financial statements have only been required to be submitted in the past four years, a significant portion were not audited. KPMG and the Department concluded that only the audited financial statements would be reliable enough to be useful. Only twenty-nine audited financial statements of schools which have closed for what could be reasonably assumed to be financial reasons were available.

Trend analysis would also be valuable in developing and testing the recommended methodology. We were unable to incorporate such analysis for two reasons. First, new accounting standards like Statement of Financial Accounting Standards (SFAS) Nos. 116 and 117 made comparison of the current year data of private not-profit institutions to prior years impossible (at least without elaborate additional effort). In addition, the unavailability of prior years? audited financial statements, particularly the prior year?s financial statements of the institutions represented in this sample group, prevented KPMG from employing trend analysis as a tool.